Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Produk Detail:
  • Author : Harsh S. Dhiman
  • Publisher : Academic Press
  • Pages : 216 pages
  • ISBN : 0128213671
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Download or Read online Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction full in PDF, ePub and kindle. this book written by Harsh S. Dhiman and published by Academic Press which was released on 21 January 2020 with total page 216 pages. We cannot guarantee that Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance. Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation. Features various supervised machine learning based regression models Offers global case studies for turbine wind farm layouts Includes state-of-the-art models and methodologies in wind forecasting

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
  • Author : Harsh S. Dhiman,Dipankar Deb,Valentina E. Balas
  • Publisher : Academic Press
  • Release : 21 January 2020
GET THIS BOOK Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book,

Renewable Energy Optimization Planning and Control

Renewable Energy Optimization  Planning and Control
  • Author : Anita Khosla,Monika Aggarwal
  • Publisher : Springer Nature
  • Release : 09 November 2021
GET THIS BOOK Renewable Energy Optimization Planning and Control

This book gathers selected high-quality research papers presented at International Conference on Renewable Technologies in Engineering (ICRTE 2021) organized by Manav Rachna International Institute of Research & Studies, Faridabad, Haryana, India, during 15–16 April 2021. The book includes conference papers on the theme “Computational Techniques for Renewable Energy Optimization”, which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of renewable energy integration, planning, control and optimization. It also provides

Soft Computing Applications

Soft Computing Applications
  • Author : Valentina Emilia Balas,Lakhmi C. Jain,Marius Mircea Balas,Shahnaz N. Shahbazova
  • Publisher : Springer Nature
  • Release : 14 August 2020
GET THIS BOOK Soft Computing Applications

This book presents the proceedings of the 8th International Workshop on Soft Computing Applications, SOFA 2018, held on 13–15 September 2018 in Arad, Romania. The workshop was organized by Aurel Vlaicu University of Arad, in conjunction with the Institute of Computer Science, Iasi Branch of the Romanian Academy, IEEE Romanian Section, Romanian Society of Control Engineering and Technical Informatics – Arad Section, General Association of Engineers in Romania – Arad Section and BTM Resources Arad. The papers included in these proceedings, published post-conference, cover the

Advances in Computational Intelligence

Advances in Computational Intelligence
  • Author : Ignacio Rojas,Gonzalo Joya,Joan Cabestany
  • Publisher : Springer
  • Release : 21 June 2013
GET THIS BOOK Advances in Computational Intelligence

This two-volume set LNCS 7902 and 7903 constitutes the refereed proceedings of the 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, held in Puerto de la Cruz, Tenerife, Spain, in June 2013. The 116 revised papers were carefully reviewed and selected from numerous submissions for presentation in two volumes. The papers explore sections on mathematical and theoretical methods in computational intelligence, neurocomputational formulations, learning and adaptation emulation of cognitive functions, bio-inspired systems and neuro-engineering, advanced topics in computational intelligence and applications

Discovery Science

Discovery Science
  • Author : Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Petit
  • Publisher : Springer
  • Release : 22 October 2012
GET THIS BOOK Discovery Science

This book constitutes the refereed proceedings of the 15th International Conference on Discovery Science, DS 2012, held in Lyon, France, in October 2012. The 22 papers presented in this volume were carefully reviewed and selected from 46 submissions. The field of discovery science aims at inducing and validating new scientific hypotheses from data. The scope of this conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, tools for supporting the human process